Currently, a number of tests exist that can diagnose whether a patient has a normal glucose tolerance (NGT) or an impaired glucose tolerance (IGT). One test is to measure the blood glucose level at a 2-hour point of OGTT: elevated blood glucose above 140 mg/dL indicates abnormal glucose tolerance. Other tests that may indicate abnormal glucose tolerance include measuring levels of fasting blood glucose, insulin, pro-insulin, c-peptide, HbA1C, fructosamine, glycation gap, 1,5-Anhydroglucitol (1,5 AG), OGTT, “clamp-like index” (CLIX) scoring (an index obtained from plasma OGTT glucose and C-peptide levels and serum creatinine), homeostasis model assessment-estimated insulin resistance (HOMA IR) scoring, and immuno reactive insulin (IRI) scores based on combinations of alpha hydroxybutyrate (AHB), linoleoyl-GPC (L-GPC), and oleic acid weighted by insulin or body mass index (BMI). The above tests, used alone or in combination, can detect the presence of pre-diabetes (metabolic syndrome) and early insulin resistance in patients who are normoglycemic in fasting state.
The best current predictors of fasting normoglycemic patients who may be at risk of developing diabetes are OGTTs and CLIX scoring of OGTTs. Both techniques involve testing multiple analytes at multiple time-points, requiring the patient to have a blood sample drawn at baseline (fasting) and to drink a beverage containing a known quantity of glucose, and subsequently contacting patient blood samples and measuring the levels of various analytes (e.g. glucose, insulin, pro-insulin, c-peptide, creatinine) at fasting baseline and at various time intervals after dosing with the glucose load. Most OGTTs and CLIX scoring require a patient to remain in the doctor's office for 2 hours post dose, and most clinicians only test baseline samples and compare to the testing at the 2 hour time point, not the labor-intensive additional blood draws for 3-5 times during the 2-hour period necessary for the CLIX scoring, due to labor and cost constraints. Moreover, complicated and laborious mathematical calculations need to be performed in order to optimize detection of at-risk individuals with these techniques. Additionally, kidney function (approximated by blood creatinine levels/eGFR) needs to be accounted for with these techniques, requiring a further step.
Thus, there is a need in the art for diagnostic biomarkers and tests that can identify patients at risk of developing Type 2 diabetes as well as the risk of disease progression in patients with insulin resistance.